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Reformat output data format to make it more general for other binaries (#9555)

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Summary:
This is to simplify the data format during benchmarking. After this change, we can use the same benchmarking harness data conversion method to parse data from multiple binaries.

This change should be coordinated with the PR: https://github.com/facebook/FAI-PEP/pull/63
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9555

Reviewed By: pjh5

Differential Revision: D8903024

Pulled By: sf-wind

fbshipit-source-id: 61cabcff99f0873729142ec6cb6dc230c685d13a
This commit is contained in:
Fei Sun 2018-07-23 10:54:25 -07:00 committed by Facebook Github Bot
parent 029cf1d78a
commit 14d4bdb406
1 changed files with 40 additions and 43 deletions
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83
modules/observers/net_observer_reporter_print.cc
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@ -14,63 +14,60 @@ void NetObserverReporterPrint::report(
NetBase* net,
std::map<std::string, PerformanceInformation>& info) {
// Not allowed to use json library
std::map<
std::string,
std::map<std::string, std::map<std::string, std::string>>>
caffe2_perf;
std::vector<std::map<std::string, std::string>> caffe2_perf;
for (auto& p : info) {
if ((p.first == "NET_DELAY") && (info.size() == 1)) {
// for Net_delay perf
caffe2_perf["NET"] = {
{"latency",
{{"value", caffe2::to_string(p.second.latency * 1000)},
{"unit", "us"}}},
{"flops", {{"value", "-1"}, {"unit", "flops"}}}};
caffe2_perf.push_back(
{{"type", "NET"},
{"value", caffe2::to_string(p.second.latency * 1000)},
{"unit", "us"},
{"metric", "latency"}});
} else if (p.first != "NET_DELAY") {
// for operator perf
std::string shape_str = get_tensor_shapes(p.second);
std::string args_str = get_op_args(p.second);
caffe2_perf[p.first] = {
{"latency",
{{"value", caffe2::to_string(p.second.latency * 1000)},
{"unit", "us"}}},
{"flops",
{{
"value",
caffe2::to_string(p.second.flops),
},
{"unit", "flops"}}},
{"tensor_shapes", {{"info_string", shape_str}, {"unit", ""}}},
{"op_args", {{"info_string", args_str}, {"unit", ""}}}};
caffe2_perf.push_back(
{{"type", p.first},
{"value", caffe2::to_string(p.second.latency * 1000)},
{"unit", "us"},
{"metric", "latency"}});
if (p.second.flops > 0) {
caffe2_perf.push_back({{"type", p.first},
{"value", caffe2::to_string(p.second.flops)},
{"unit", "flop"},
{"metric", "flops"}});
}
if (shape_str != "") {
caffe2_perf.push_back({{"type", p.first},
{"info_string", shape_str},
{"unit", ""},
{"metric", "tensor_shapes"}});
}
if (args_str != "") {
caffe2_perf.push_back({{"type", p.first},
{"info_string", args_str},
{"unit", ""},
{"metric", "op_args"}});
}
}
}
for (auto it = caffe2_perf.begin(); it != caffe2_perf.end(); it++) {
std::stringstream buffer;
auto entry = *it;
buffer << IDENTIFIER << "{";
buffer << "\"" << it->first << "\""
<< ": {";
for (auto jt = it->second.begin(); jt != it->second.end(); jt++) {
buffer << "\"" << jt->first << "\""
<< ": {";
for (auto kt = jt->second.begin(); kt != jt->second.end(); kt++) {
buffer << "\"" << kt->first << "\""
<< ": "
<< "\"" << kt->second << "\"";
auto lt = kt;
if ((++lt) != jt->second.end()) {
buffer << ", ";
}
}
buffer << "}";
auto lt = jt;
if ((++lt) != it->second.end()) {
buffer << ", ";
}
buffer << "\"type\": \"" << entry["type"] << "\","
<< "\"unit\": \"" << entry["unit"] << "\","
<< "\"metric\": \"" << entry["metric"] << "\",";
if (entry.find("value") != entry.end()) {
buffer << "\"value\": \"" << entry["value"] << "\"";
} else if (entry.find("info_string") != entry.end()) {
buffer << "\"info_string\": \"" << entry["info_string"] << "\"";
}
buffer << "}}";
buffer << "}";
LOG(INFO) << buffer.str();
}
}
@ -90,7 +87,7 @@ static std::string get_tensor_shapes(PerformanceInformation p) {
shape_stream << "]";
shape_str = shape_stream.str();
} else {
shape_str = "[]";
shape_str = "";
}
return shape_str;
}
@ -118,7 +115,7 @@ static std::string get_op_args(PerformanceInformation p) {
args << "]";
args_str = args.str();
} else {
args_str = "[]";
args_str = "";
}
return args_str;
}